My research focuses on the theoretical aspects of algorithms, computational geometry, and machine learning.
In the tradition of theoretical computer science, authors are listed in alphabetical order.
Dimension-independent Rates for Structured Neural Density Estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
International Conference on Machine Learning (ICML) 2025
Near-optimal Active Regression of Single-Index Models
Yi Li, Wai Ming Tai (alphabetical order)
International Conference on Learning Representations (ICLR) 2025
Breaking the Curse of Dimensionality in Structured Density Estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
Conference on Neural Information Processing Systems (NeurIPS) 2024
Agnostic Active Learning of Single Index Models with Linear Sample Complexity
Aarshvi Gajjar*, Wai Ming Tai*, Xingyu Xu*, Chinmay Hegde, Christopher Musco, Yi Li (* = equal contribution)
Conference on Learning Theory (COLT) 2024
Optimal Estimation of Gaussian (Poly)trees
Yuhao Wang, Ming Gao*, Wai Ming Tai*, Bryon Aragam, Arnab Bhattacharyya (* = equal contribution)
International Conference on Artificial Intelligence and Statistics (AISTATS) 2024
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
International Conference on Artificial Intelligence and Statistics (AISTATS) 2024
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures
Bryon Aragam, Wai Ming Tai (alphabetical order)
Conference on Learning Theory (COLT) 2023
Learning Mixtures of Gaussians with Censored Data
Wai Ming Tai, Bryon Aragam
International Conference on Machine Learning (ICML) 2023
Optimal Coreset for Gaussian Kernel Density Estimation
Wai Ming Tai
Symposium on Computational Geometry (SOCG) 2022
Optimal Estimation of Gaussian DAG Models
Ming Gao, Wai Ming Tai, Bryon Aragam
International Conference on Artificial Intelligence and Statistics (AISTATS) 2022
Finding an Approximate Mode of a Kernel Density Estimate
Jasper C.H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai (alphabetical order)
European Symposium on Algorithms (ESA) 2021
The GaussianSketch for Almost Relative Error Kernel Distance
Jeff M. Phillips, Wai Ming Tai (alphabetical order)
International Conference on Randomization and Computation (RANDOM) 2020
Approximate Guarantees for Dictionary Learning
Aditya Bhaskara, Wai Ming Tai (alphabetical order)
Conference on Learning Theory (COLT) 2019
Near-Optimal Coresets of Kernel Density Estimates
Jeff M. Phillips, Wai Ming Tai (alphabetical order)
Symposium on Computational Geometry (SOCG) 2018 (Invited to DCG Special Issue)
Discrete & Computational Geometry (DCG) 2020
Improved Coresets for Kernel Density Estimates
Jeff M. Phillips, Wai Ming Tai (alphabetical order)
Symposium on Discrete Algorithms (SODA) 2018
Tracking the Frequency Moments at All Times
Zengfeng Huang, Wai Ming Tai, Ke Yi (alphabetical order)
Unpublished
Reviewer for: NeurIPS, ICML, ICLR, AISTATS, AAAI, ALT, STOC, FOCS, SODA, SOCG, ESA, ICALP